Artificial intelligence in public health prevention of legionelosis in drinking water systems.

Peter Sinčak,Jaroslav Ondo,Jakub Sabol,Daniela Kaposztasova,Maria Virčikova,Zuzana Vranayova

doi:10.3390/ijerph110808597

Abstract

Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives.

Highlights

The primary goal of the paper is to present our research on a theoretical and experimental analysis of hot water distribution systems focused on reducing the microbiological risk in water systems in conditions prevailing in the Slovak Republic by using artificial intelligence principles
In order to control the water temperature and the risk of Legionella colonization in the system, we propose an approach based on neural networks which have as input the flow and temperature to the water tank and the output is a temperature profile of the water tank and selected nodes of the water system
After the implementation of extensions of the very first program, we can adapt the simulator to similar hot water systems and we can predict the temperature profile in these systems

Summary

Introduction

The primary goal of the paper is to present our research on a theoretical and experimental analysis of hot water distribution systems focused on reducing the microbiological risk in water systems in conditions prevailing in the Slovak Republic by using artificial intelligence principles. During the years 1985–2013 there were 88 reported cases (55 male, 33 females) in our country [4], eight of them mortal. Extensive study of this problem in Slovakia can be found in [5]. The conditions that encourage the bacterial grow are: bacteria presence, a temperature range between 20–50 °C, slow moving or stagnant water, adequate food source, formation of aerosols, and the presence of people. We must break this chain to ensure that the system will be safe. The challenge is to create a community access system which will be able to simulate a virtual water distribution system in the building, including a water tank temperature profile which is the most common place for bacterial occurrence

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Conclusion